Identification of risk factors for increased cost, charges, and length of stay for cardiac patients

MaWhinney, Samantha; Brown, E. Richard; Malcolm, J.; Villanueva, Claudia; Groves, Bertron Μ.; Quaife, Robert A.; Lindenfeld, JoAnn; Warner, Bradley A.; Hammermeister, Karl E.; Grover, Frederick L.; Shroyer, A. Laurie

doi:10.1016/s0003-4975(00)01510-1

Cited by 27 publications

(21 citation statements)

References 12 publications

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“…Finally, because there was no direct method of controlling for the severity of disease, LOS was used as a proxy in the statistical model because this variable correlates with clinical risk. 14 Although there are numerous studies of primary care initiatives that have demonstrated a reduction in readmissions, these have relied on the extended capacity of hospitals and clinics to reach out to patients following discharge. [20][21][22][23] The results presented here demonstrate that telephonic counseling delivered shortly after hospital discharge can successfully reduce readmissions as well.…”

Section: Discussionmentioning

confidence: 99%

“…LOS during the initial hospitalization was used as a proxy for disease severity because clinical risk is associated with the number of days spent in the hospital. 14 The readmission window was 30 days for all members of the comparison group; however, because the length of the readmission window for members in the intervention group varied between 16 and 29 days based on when discharge calls were received, it was necessary to adjust for this disparity to ensure equal comparison of the two groups. All 28 HARRISON ET AL.…”

Section: Methodsmentioning

confidence: 99%

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The Impact of Postdischarge Telephonic Follow-Up on Hospital Readmissions

Harrison

Hara

Pope

et al. 2011

Population Health Management

131

103

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Recurrent hospitalizations are responsible for considerable health care spending, although prior studies have shown that a substantial proportion of readmissions are preventable through effective discharge planning and patient follow-up after the initial hospital visit. This retrospective cohort study was undertaken to determine whether telephonic outreach to ensure patient understanding of and adherence to discharge orders following a hospitalization is effective at reducing hospital readmissions within 30 days after discharge. Claims data were analyzed from 30,272 members of a commercial health plan who were discharged from a hospital in 2008 to determine the impact of telephonic intervention on the reduction of 30-day readmissions. Members who received a telephone call within 14 days of discharge and were not readmitted prior to that call comprised the intervention group; all other members formed the comparison group. Multiple logistic regression was used to determine the impact of the intervention on 30-day readmissions, after adjusting for covariates. Results demonstrated that older age, male sex, and increased initial hospitalization length of stay were associated with an increased likelihood of readmission (P < 0.001). Receipt of a discharge call was associated with reduced rates of readmission; intervention group members were 23.1% less likely than the comparison group to be readmitted within 30 days of hospital discharge (P ¼ 0.043). These findings indicate that timely discharge follow-up by telephone to supplement standard care is effective at reducing near-term hospital readmissions and, thus, provides a means of reducing costs for health plans and their members. (Population Health Management

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Impact of Postdischarge Telephonic Follow-Up on Hospital Readmissions

Harrison

Hara

Pope

et al. 2011

Population Health Management

131

103

View full text Add to dashboard Cite

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“…PH models have been used to determine predictors of cost for patients admitted for CABG surgery [11] and for ischaemic heart disease [12]. In both of these studies, complete cases were those that were discharged home alive (that is those for whom complete costs were observed) and in-hospital deaths were treated as censored observations.…”

Section: Cox Proportional Hazards Regressionmentioning

confidence: 99%

A comparison of several regression models for analysing cost of CABG surgery

Austin

Ghali

2003

Statistics in Medicine

123

111

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Investigators in clinical research are often interested in determining the association between patient characteristics and cost of medical or surgical treatment. However, there is no uniformly agreed upon regression model with which to analyse cost data. The objective of the current study was to compare the performance of linear regression, linear regression with log-transformed cost, generalized linear models with Poisson, negative binomial and gamma distributions, median regression, and proportional hazards models for analysing costs in a cohort of patients undergoing CABG surgery. The study was performed on data comprising 1959 patients who underwent CABG surgery in Calgary, Alberta, between June 1994 and March 1998. Ten of 21 patient characteristics were significantly associated with cost of surgery in all seven models. Eight variables were not significantly associated with cost of surgery in all seven models. Using mean squared prediction error as a loss function, proportional hazards regression and the three generalized linear models were best able to predict cost in independent validation data. Using mean absolute error, linear regression with log-transformed cost, proportional hazards regression, and median regression to predict median cost, were best able to predict cost in independent validation data. Since the models demonstrated good consistency in identifying factors associated with increased cost of CABG surgery, any of the seven models can be used for identifying factors associated with increased cost of surgery. However, the magnitude of, and the interpretation of, the coefficients vary across models. Researchers are encouraged to consider a variety of candidate models, including those better known in the econometrics literature, rather than begin data analysis with one regression model selected a priori. The final choice of regression model should be made after a careful assessment of how best to assess predictive ability and should be tailored to the particular data in question.

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“…Waiting times for heart transplantation [25], time to discharge following hospital admission [26] and time from hospital arrival to initiation of thrombolysis [27] have all been analysed using proportional hazards regression models. Given an event of interest, the hazard function h(t), describes the instantaneous rate of the occurrence of the event at time t, given that the subject has survived until time t. Proportional hazards regression models the hazard of an outcome of interest.…”

Section: Proportional Hazards Regressionmentioning

confidence: 99%

The use of quantile regression in health care research: a case study examining gender differences in the timeliness of thrombolytic therapy

Austin

Daly

et al. 2004

Statistics in Medicine

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Investigators are frequently interested in determining patient and system characteristics associated with delays in the provision of essential medical treatment. Investigators have typically used either multiple linear regression or Cox proportional hazards models to assess the impact of patient and system characteristics on the timeliness of medical treatment. A drawback to the use of these two methods is that they allow, at best, a partial exploration of how a distribution of delays in treatment or of waiting times changes with patient characteristics. In contrast, quantile regression models allow one to assess how any quantile of a conditional distribution changes with patient characteristics. We illustrate the utility of quantile regression by examining gender differences in the delivery of thrombolysis in patients with an acute myocardial infarction. We demonstrate that richer inferences can be drawn through the use of quantile regression. Females were more likely to experience delays in treatment compared to males. Furthermore, gender had a greater impact upon those patients who had the greatest delays in treatment. Investigators who want to determine how a distribution of delays in treatment or of waiting times changes with patient or system characteristics should consider complementing their analyses with the use of quantile regression.

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Identification of risk factors for increased cost, charges, and length of stay for cardiac patients

Cited by 27 publications

References 12 publications

The Impact of Postdischarge Telephonic Follow-Up on Hospital Readmissions

The Impact of Postdischarge Telephonic Follow-Up on Hospital Readmissions

A comparison of several regression models for analysing cost of CABG surgery

The use of quantile regression in health care research: a case study examining gender differences in the timeliness of thrombolytic therapy

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