Characteristics of COVID-19 patients admitted to a tertiary care hospital in Pune, India, and cost-effective predictors of intensive care treatment requirement

Abstract

BackgroundMaharashtra is one of the worst affected states in this pandemic.2 As of 30th September, Maharashtra has in total 1.4 million cases with 38,000 deaths. Objective was to study associations of severity of disease and need for ICU treatment in COVID-19 patients.MethodsA retrospective study of clinical course in 800 hospitalized COVID-19 patients, and a predictive model of need for ICU treatment. Eight hundred consecutive patients admitted with confirmed COVID-19 disease.ResultsAverage age was 41 years, 16% were <20 years of age, 55% were male, 50% were asymptomatic and 16% had at least one comorbidity. Using MoHFW India severity guidelines, 73% patients had mild, 6% moderate and 20% severe disease. Severity was associated with higher age, symptomatic presentation, elevated neutrophil and reduced lymphocyte counts and elevated inflammatory markers. Seventy-seven patients needed ICU treatment: they were older (56 years), more symptomatic and had lower SpO2 and abnormal chest X-ray and deranged hematology and biochemistry at admission. A model trained on the first 500 patients, using above variables predicted need for ICU treatment with sensitivity 80%, specificity 88% in subsequent 300 patients; exclusion of expensive laboratory tests did not affect accuracy.ConclusionIn the early phase of COVID- 19 epidemic, a significant proportion of hospitalized patients were young and asymptomatic. Need for ICU treatment was predicted by simple measures including higher age, symptomatic onset, low SpO2 and abnormal chest X-ray. We propose a cost-effective model for referring patients for treatment at specialized COVID-19 hospitals.Key MessagesOf 800 patients, 73% had mild, 6% moderate and 20% had severe disease.Seventy-seven patients (9.6%) required ICU treatment, 25 (3%) died.ICU treatment was predicted by higher age, more symptomatic presentation, lower SpO2 and pneumonia on chest X-ray at admission.A machine learning model features in first 500 patients accurately predicted ICU treatment in subsequent 300 patients.A good clinical protocol, SpO2 and chest X-ray are adequate to predict and triage COVID-19 patients for hospital admissions in resource poor environments.

Profile of COVID-19 patients in COVID dedicated tertiary care centre in Himachal Pradesh: a prospective observational cross sectional study

“…7% in mechanically ventilated patients 13. Death rate in a study conducted by Shukla et al mentions death rate of 3% in their study while death rate in our study was 6.9% 11. 8 patients were >60 years old and 6 patients were aged 30-60 years group in our study while in USA reports described outcome in aged >85 years 10-27%, 3-11% among aged 65-84 years, 1-3% among aged 55-64 years, <1% in aged 20-54 years 12.…”

Severity and mortality prediction models to triage Indian COVID-19 patients

“…To the best of our knowledge, only two such models have been trained and tested on Indian populations. First, a model based on a Random Forests classifier predicting ICU admissions using features such as age, symptoms at diagnosis, number of comorbidities, chest X-ray, SpO2 concentration, ANC/ALC ratio, CRP, and Serum ferritin concentrations [ 3 ]. Second, a set of models forecast the number of COVID-19 cases in India using simple SEIR mathematical models and also using LSTMs [ 4 ], [ 5 ].…”